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A Novel Probabilistic Data Flow Framework

Abstract

Classical data flow analysis determines whether a data flow fact may hold or does not hold at some program point. Probabilistic data flow systems compute a range, i.e. a probability, with which a data flow fact will hold at some program point. In this paper we develop a novel, practicable framework for probabilistic data flow problems. In contrast to other approaches, we utilize execution history for calculating the probabilities of data flow facts. In this way we achieve significantly better results. Effectiveness and efficiency of our approach are shown by compiling and running the SPECint95 benchmark suite.

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Authors
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Citation
Category
Technical Report (Technical Report)
Divisions
Scientific Computing
Publisher
Institute for Software Science, University of Vienna
Date
January 2001
Official URL
http://www.par.univie.ac.at/publications/download/...
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